气候变化领域集成服务门户(CCPortal): Machine learning in space and time for modelling soil organic carbon change

CCPortal

DOI	10.1111/ejss.12998
	Machine learning in space and time for modelling soil organic carbon change
	Heuvelink G.B.M.; Angelini M.E.; Poggio L.; Bai Z.; Batjes N.H.; van den Bosch R.; Bossio D.; Estella S.; Lehmann J.; Olmedo G.F.; Sanderman J.
发表日期	2020
ISSN	13510754
英文摘要	Spatially resolved estimates of change in soil organic carbon (SOC) stocks are necessary for supporting national and international policies aimed at achieving land degradation neutrality and climate change mitigation. In this work we report on the development, implementation and application of a data-driven, statistical method for mapping SOC stocks in space and time, using Argentina as a pilot. We used quantile regression forest machine learning to predict annual SOC stock at 0–30 cm depth at 250 m resolution for Argentina between 1982 and 2017. The model was calibrated using over 5,000 SOC stock values from the 36-year time period and 35 environmental covariates. We preprocessed normalized difference vegetation index (NDVI) dynamic covariates using a temporal low-pass filter to allow the SOC stock for a given year to depend on the NDVI of the current as well as preceding years. Predictions had modest temporal variation, with an average decrease for the entire country from 2.55 to 2.48 kg C m−2 over the 36-year period (equivalent to a decline of 211 Gg C, 3.0% of the total 0–30 cm SOC stock in Argentina). The Pampa region had a larger estimated SOC stock decrease from 4.62 to 4.34 kg C m−2 (5.9%) during the same period. For the 2001–2015 period, predicted temporal variation was seven-fold larger than that obtained using the Tier 1 approach of the Intergovernmental Panel on Climate Change and United Nations Convention to Combat Desertification. Prediction uncertainties turned out to be substantial, mainly due to the limited number and poor spatial and temporal distribution of the calibration data, and the limited explanatory power of the covariates. Cross-validation confirmed that SOC stock prediction accuracy was limited, with a mean error of 0.03 kg C m−2 and a root mean squared error of 2.04 kg C m−2. In spite of the large uncertainties, this work showed that machine learning methods can be used for space–time SOC mapping and may yield valuable information to land managers and policymakers, provided that SOC observation density in space and time is sufficiently large. © 2020 The Authors. European Journal of Soil Science published by John Wiley & Sons Ltd on behalf of British Society of Soil Science.
英文关键词	Argentina; carbon stock; climate change; land degradation; quantile regression forest; space–time mapping
来源期刊	European Journal of Soil Science
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/176701
作者单位	ISRIC – World Soil Information, Wageningen, Netherlands; Soil Geography and Landscape Group, Wageningen University, Wageningen, Netherlands; Instituto Nacional de Tecnologia Agropecuaria, Buenos Aires, Argentina; The Nature Conservancy, Arlington, VA, United States; Vizzuality, Madrid, Spain; Soil and Crop Sciences, Cornell University, Ithaca, NY, United States; Woods Hole Research Center, Falmouth, MA, United States
推荐引用方式 GB/T 7714	Heuvelink G.B.M.,Angelini M.E.,Poggio L.,et al. Machine learning in space and time for modelling soil organic carbon change[J],2020.
APA	Heuvelink G.B.M..,Angelini M.E..,Poggio L..,Bai Z..,Batjes N.H..,...&Sanderman J..(2020).Machine learning in space and time for modelling soil organic carbon change.European Journal of Soil Science.
MLA	Heuvelink G.B.M.,et al."Machine learning in space and time for modelling soil organic carbon change".European Journal of Soil Science (2020).